Structural damage identification of high-order shear beams based on a genetic algorithm

Abstract

The beam structure is the main load-bearing structure of engineering projects. High-order shear beams are widely used in engineering. Therefore, damage identification of beam structures is important to guarantee project quality and life safety. To identify the location and depth of cracks in a beam structure, a genetic algorithm (GA) and a damage identification model are combined. This method optimises the back-propagation neural network by using the ability of the GA to find the global optimal solution. The natural frequency (NF) of the cracked beam is obtained through finite-element analysis, and the NF is taken as the input of the model, and the crack location and depth are taken as the outputs of the model. In the experiment, it is found through regression analysis that the predicted output value of the model has a high coincidence with the real value, and its regression coefficient reaches 0.99842. Through an example analysis, the sum of squares of the prediction error of the model is 5.6. The average relative errors of the beam crack location and crack depth are 0.54 and 4.15%, respectively. The experimental results show that the proposed model has a high prediction accuracy and can accurately identify damage to the beam structure.